Monitoring, particularly in harsh environments, is a challenge in many industry applications. The ability to provide reliable data in real-time in such harsh environments has applicability in jet engines, power generation gas turbines, industry finances, and manufacturing processes and the like.
For illustrative purposes, electric power generation is described herein. By its very nature, large scale electric power generation using fossil fuels involves processes that occur at extremes of both temperature and pressure conditions. Monitoring these processes, and the condition of the equipment in which they occur, is of paramount importance for reliable, cost-effective, efficient power generation. Monitoring of turbine machinery used in power generation would be particularly useful. Modern gas turbine achieve higher efficiency and reduced fuel consumptions by using higher combustion temperatures, hotter coolant temperatures from higher pressure ratios, and reduced cooling flow rates. In addition, higher combustion temperatures adversely affect the availability, reliability, and safety of gas turbines, particularly the life of high cost, critical hot gas path (HGP) components. The life of power system components working under harsh environments depends strongly on temperature and strain. Improved temperature measurement accuracy would directly translate into an improved turbine operating efficiency due to the ability to safely operate the machine close to its operating limits. High fidelity inputs for physics based lifing models would enable order-of-magnitude improvements in lifing prediction accuracy. In most advanced gas turbines, combustion temperatures exceed the melting point of the HGP components, and these components must be actively cooled. Large design safety margins are generally incorporated for these critical components. High fidelity real-time data would improve confidence in design safety margins and potentially shorten the turbine design cycle. Multi-property online measurements during the power system tuning phase would enable designers to better assess critical component lifing and potential compensating design modifications. The accurate, real-time, simultaneous measurement of strain and temperature in the monitoring of power systems/component may improve prediction accuracy of physics based lifing models. This improved accuracy may translate into improved turbine operating efficiency due to the ability to safely operate the machine closer to its operating limits. In one example, calculations show a 10% reduction in cooling flow can lead to up to $9 million of savings during the 20-year operating life of one combined cycle gas turbine.
Despite the need to obtain accurate online temperature and strain data from within an operating turbine machine, achieving this goal has remained quite elusive. Within the past decade, development of rare-earth based light emitting materials that are suitable as optical (luminescence-based) thermometers has enabled some progress to be made; however, issues of relatively high cost and poor sensitivity at very high-temperatures remain. In addition, the online optical detection of strain has not yet been adequately addressed. There is a continued desire to address this monitoring need by developing a low cost, non-intrusive, multi-functional embedded sensor system for online assessment of power systems.